Process monitoring, especially fault diagnosis, is an indispensable component in terms of process safety and profit. Since transfer entropy as a data-driven technique for fault diagnosis was proposed in the early 2000s, many attempts for application to chemical processes were reported. However, the application was limited because it requires high computational cost owing to the increased complexity and scale of chemical processes. This paper proposes an integrated transfer entropy method with the graphical lasso, a regularized optimization method for relevant subset selection. The proposed methodology exploits the outstanding performance of transfer entropy in diagnosis while mitigating the disadvantages in terms of the computational cost via the graphical lasso. To illustrate the effectiveness of the proposed method, two case studies were carried out. The SCR system, a catalytic after-treatment system for automobile exhaust gas, was examined to verify the feasibility of the proposed method. Then, the Tennessee Eastman process, which is a benchmark process, was also tested as a representative of the industrial-scale chemical process. The results of the proposed method demonstrate that in all cases it significantly reduces the computational cost of root cause analysis using transfer entropy, and even outperforms the conventional transfer entropy method for diagnosis in some particular cases.

就过程安全性和利润而言，过程监控，尤其是故障诊断是必不可少的组件。由于在2000年代初期提出了将传输熵作为一种数据驱动的故障诊断技术，因此报道了许多在化学过程中的应用尝试。但是，由于化学过程的复杂性和规模的增加，由于需要很高的计算成本，因此应用受到限制。本文提出了一种带有图形套索的集成传递熵方法，一种针对相关子集选择的正则化优化方法。所提出的方法利用了传递熵在诊断中的出色表现，同时通过图形套索减轻了计算成本方面的缺点。为了说明所提出方法的有效性，进行了两个案例研究。对SCR系统（一种用于汽车尾气的催化后处理系统）进行了研究，以验证该方法的可行性。然后，作为基准过程的田纳西州伊士曼过程也被测试为工业规模化学过程的代表。所提出方法的结果表明，在所有情况下，它都可以大大降低使用转移熵进行根本原因分析的计算成本，甚至在某些特定情况下甚至优于传统的转移熵方法进行诊断。还被测试为工业规模化学过程的代表。所提出方法的结果表明，在所有情况下，它都可以大大降低使用转移熵进行根本原因分析的计算成本，甚至在某些特定情况下甚至优于传统的转移熵方法进行诊断。还被测试为工业规模化学过程的代表。所提出方法的结果表明，在所有情况下，它都可以大大降低使用转移熵进行根本原因分析的计算成本，甚至在某些特定情况下甚至优于传统的转移熵方法进行诊断。